提出可忠实追踪的通用概念解释方法，提升模型可解释性。
FaCT: Faithful Concept Traces for Explaining Neural Network Decisions
- 基于模型内在机制提取跨类别共享概念
- 概念贡献与可视化可精准追溯到每一层
- 新评估指标验证解释一致性，适合可信AI研究者
深度网络在众多任务中表现卓越，但获得全局概念级理解仍是关键挑战。现有后处理概念解释方法常不忠实于模型，且对概念设定强假设，如类别专属、空间范围小或符合人类预期。本文强调解释的忠实性，提出一种具备模型内生机制的概念解释新方法。概念跨类别共享，可从任意层追溯其对输出logit的贡献及输入可视化。同时利用基础模型提出新的概念一致性度量C²-Score，用于评估概念解释方法。实验表明，相比以往方法，本方法在概念一致性上更优，用户认为解释更易懂，同时保持ImageNet上的竞争力性能。
原文摘要 · Abstract (English)
Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they function remains a key challenge. Many post-hoc concept-based approaches have been introduced to understand their workings, yet they are not always faithful to the model. Further, they make restrictive assumptions on the concepts a model learns, such as class-specificity, small spatial extent, or alignment to human expectations. In this work, we put emphasis on the faithfulness of such concept-based explanations and propose a new model with model-inherent mechanistic concept-explanations. Our concepts are shared across classes and, from any layer, their contribution to the logit and their input-visualization can be faithfully traced. We also leverage foundation models to propose a new concept-consistency metric, C$^2$-Score, that can be used to evaluate concept-based methods. We show that, compared to prior work, our concepts are quantitatively more consistent and users find our concepts to be more interpretable, all while retaining competitive ImageNet performance.
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